Correlation study of time-varying multivariate climate data sets

Jeffrey Sukharev, Chaoli Wang, Kwan-Liu Ma, Andrew T. Wittenberg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Scopus citations


We present a correlation study of time-varying multivariate volumetric data sets. In most scientific disciplines, to test hypotheses and discover insights, scientists are interested in looking for connections among different variables, or among different spatial locations within a data field. In response, we propose a suite of techniques to analyze the correlations in time-varying multivariate data. Various temporal curves are utilized to organize the data and capture the temporal behaviors. To reveal patterns and find connections, we perform data clustering and segmentation using the kmeans clustering and graph partitioning algorithms. We study the correlation structure of a single or a pair of variables using pointwise correlation coefficients and canonical correlation analysis. We demonstrate our approach using results on time-varying multivariate climate data sets.

Original languageEnglish (US)
Title of host publicationIEEE Pacific Visualization Symposium, PacificVis 2009 - Proceedings
Number of pages8
StatePublished - Jul 21 2009
EventIEEE Pacific Visualization Symposium, PacificVis 2009 - Beijing, China
Duration: Apr 20 2009Apr 23 2009


OtherIEEE Pacific Visualization Symposium, PacificVis 2009


  • G.3 [probability and statistics]: Multivariate statistics
  • G.3 [probability and statistics]: Time series statistics
  • J.2 [physical sciences and engineering]: Earth and atmospheric sciences

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software


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